Remote Data Scientist - Enterprise Analytics & GTM

Warmada; United States; Remote; USD 86,000 - 176,000; Full time

Benefits offered by this job

Flexible Paid Time Off

Equity Compensation

Employee Stock Purchase Plan

Growth and Development Fund

Parental Leave

Job summary

GitLab is seeking a Data Scientist for the Enterprise Analytics team in the United States. You will embed with Sales, Revenue Operations, and Finance to shape go-to-market metrics, growth strategies, and data-driven decisions across the GTM org.

You’ll develop and productionize ML models (propensity, PQL, churn), run experiments, and support executive-level reporting and KPI tracking with Python/SQL in a high-scale environment.

Qualifications

• 3-5 years in GTM analytics or similar technical role.
• Ability to translate ambiguous business problems into clear, measurable questions.
• Experience with experimentation and causal inference (A/B testing, power analysis).
• Deep SQL proficiency in cloud data warehouses and BI tools.
• Experience with Python for statistical modeling and production data science.

Responsibilities

• Partner with sales and finance to define go-to-market metrics.
• Perform exploratory data analysis to uncover trends and drivers of activation and churn.
• Build and productionize ML models (propensity, PQL, churn) with MLOps practices.
• Design, run, and evaluate growth experiments (A/B tests) and measure impact.
• Write production-grade Python and SQL and maintain coding standards.
• Contribute to GitLab’s Semantic Layer for self-serve metrics.

Skills

SQL (Snowflake)

Python (stats, ML)

Data analytics

Experimentation

Communication with stakeholders

Pandas / NumPy

Tools

Looker

Tableau

Hex

MLflow

Job description

GitLab is seeking a Data Scientist for the Enterprise Analytics team in the United States. You will embed with Sales, Revenue Operations, and Finance to shape go-to-market metrics, growth strategies, and data-driven decisions across the GTM org.

You’ll develop and productionize ML models (propensity, PQL, churn), run experiments, and support executive-level reporting and KPI tracking with Python/SQL in a high-scale environment.